Topic embedding of sentences for story segmentation

Jia Yu, Xiong Xiao, Lei Xie, Eng Siong Chng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we propose to embed sentences into fixed-dimensional vectors that carry the topic information for story segmentation. As a sentence comprises of a sequence of words and may have different lengths, we use long short-term memory recurrent neural network (LSTM-RNN) to summarize the information of the whole sentence and only predict the topic class at the last word in the sentence. The output of the network at the last word can be used as an embedding of the sentence in the topic space. We used the obtained sentence embeddings in the HMM-based story segmentation framework and obtained promising results. On the TDT2 corpus, the F1 measure is improved to 0.789 from 0.765 which is obtained by a competitive system using DNN and bag-of-words features.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1602-1607
Number of pages6
ISBN (Electronic)9781538615423
DOIs
StatePublished - 2 Jul 2017
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 12 Dec 201715 Dec 2017

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Conference

Conference9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Country/TerritoryMalaysia
CityKuala Lumpur
Period12/12/1715/12/17

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